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Algorithms for counting the occurrences of special events in the framework of partially-observed discrete event dynamical systems (DEDS) were developed in previous work. Their performances typically become better as the sensors providing the observations become more costly or increase in number. This paper addresses the problem of finding a sensor configuration that achieves an optimal balance between cost and the performance of the special event counting algorithm, while satisfying given observability requirements and constraints. Since this problem is generally computational hard in the framework considered, a sensor optimization algorithm is developed using two greedy heuristics, one myopic and the other ...
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Description

Algorithms for counting the occurrences of special events in the framework of partially-observed discrete event dynamical systems (DEDS) were developed in previous work. Their performances typically become better as the sensors providing the observations become more costly or increase in number. This paper addresses the problem of finding a sensor configuration that achieves an optimal balance between cost and the performance of the special event counting algorithm, while satisfying given observability requirements and constraints. Since this problem is generally computational hard in the framework considered, a sensor optimization algorithm is developed using two greedy heuristics, one myopic and the other based on projected performances of candidate sensors. The two heuristics are sequentially executed in order to find best sensor configurations. The developed algorithm is then applied to a sensor optimization problem for a multiunit- operation system. Results show that improved sensor configurations can be found that may significantly reduce the sensor configuration cost but still yield acceptable performance for counting the occurrences of special events.

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